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CISPA, University of Oxford
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AI regulation can be reframed as a mechanism design problem where providers "bet" on their model's compliance, with perfect market outcomes provably linked to credal sets of non-compliant distributions.
Second-order uncertainty representations matter: set-based and distribution-based methods, often considered incomparable, can be rigorously compared, revealing how representation choices impact uncertainty-aware performance.
